Commit c33aa00e authored by Baptiste Bauvin's avatar Baptiste Bauvin
Browse files

Removed some comments

parent c9347ef4
...@@ -215,7 +215,7 @@ class MuComboClassifier(BaseEnsemble, ClassifierMixin, UBoosting): ...@@ -215,7 +215,7 @@ class MuComboClassifier(BaseEnsemble, ClassifierMixin, UBoosting):
# required by some scikit-learn classifiers (for example # required by some scikit-learn classifiers (for example
# sklearn.svm.SVC) # sklearn.svm.SVC)
dist = np.empty(cost.shape[:2], dtype=cost.dtype, order="C") dist = np.empty(cost.shape[:2], dtype=cost.dtype, order="C")
# NOTE: In Sokol's PhD thesis, the formula for dist is mistakenly given # NOTE: In Sokol Koco's PhD thesis, the formula for dist is mistakenly given
# with a minus sign in section 2.2.2 page 31 # with a minus sign in section 2.2.2 page 31
sum_cost = np.sum(cost[:, np.arange(n_samples), y], axis=1)[:, np.newaxis] sum_cost = np.sum(cost[:, np.arange(n_samples), y], axis=1)[:, np.newaxis]
sum_cost[sum_cost==0] = 1 sum_cost[sum_cost==0] = 1
...@@ -233,7 +233,6 @@ class MuComboClassifier(BaseEnsemble, ClassifierMixin, UBoosting): ...@@ -233,7 +233,6 @@ class MuComboClassifier(BaseEnsemble, ClassifierMixin, UBoosting):
indicatrice_one_yi[:, np.arange(n_samples), y_i] = 1 indicatrice_one_yi[:, np.arange(n_samples), y_i] = 1
delta = np.ones((self.n_views_, n_samples, self.n_classes_), dtype=np.int) delta = np.ones((self.n_views_, n_samples, self.n_classes_), dtype=np.int)
delta[:, np.arange(n_samples), y_i] = -1 delta[:, np.arange(n_samples), y_i] = -1
# indic_minus_one = np.where(np.arange(self.n_classes_) == y)
return indicate_ones, indicatrice_one_yi, delta return indicate_ones, indicatrice_one_yi, delta
def _compute_edges(self, cost, predicted_classes, y): def _compute_edges(self, cost, predicted_classes, y):
...@@ -268,10 +267,6 @@ class MuComboClassifier(BaseEnsemble, ClassifierMixin, UBoosting): ...@@ -268,10 +267,6 @@ class MuComboClassifier(BaseEnsemble, ClassifierMixin, UBoosting):
n_views = predicted_classes.shape[0] n_views = predicted_classes.shape[0]
n_samples = y.shape[0] n_samples = y.shape[0]
if use_coop_coef: if use_coop_coef:
# coop_coef = self._compute_coop_coef(predicted_classes, y)
# ajout mucumbo verifier les dim
# ????? coop_cof_beta = betas[predicted_classes]
increment = alphas[:, np.newaxis, np.newaxis] * betas[:, np.newaxis, :] increment = alphas[:, np.newaxis, np.newaxis] * betas[:, np.newaxis, :]
increment = np.tile(increment,(1, n_samples, 1)) increment = np.tile(increment,(1, n_samples, 1))
else: else:
...@@ -324,7 +319,6 @@ class MuComboClassifier(BaseEnsemble, ClassifierMixin, UBoosting): ...@@ -324,7 +319,6 @@ class MuComboClassifier(BaseEnsemble, ClassifierMixin, UBoosting):
------- -------
betas arrays betas arrays
""" """
# delta = self.delta_c_yi(predicted_classes, y)
indicat, indicate_yi, delta = self._indicatrice(predicted_classes, y) indicat, indicate_yi, delta = self._indicatrice(predicted_classes, y)
delta_vue = np.block(np.split(delta, self.n_views_, axis=0)).squeeze() delta_vue = np.block(np.split(delta, self.n_views_, axis=0)).squeeze()
indicate_vue = np.block(np.split(indicat, self.n_views_, axis=0)).squeeze() indicate_vue = np.block(np.split(indicat, self.n_views_, axis=0)).squeeze()
...@@ -460,10 +454,7 @@ class MuComboClassifier(BaseEnsemble, ClassifierMixin, UBoosting): ...@@ -460,10 +454,7 @@ class MuComboClassifier(BaseEnsemble, ClassifierMixin, UBoosting):
self.estimator_weights_beta_ = np.zeros((self.n_iterations_, n_views), dtype=np.float) self.estimator_weights_beta_ = np.zeros((self.n_iterations_, n_views), dtype=np.float)
self.estimator_errors_ = np.array([], dtype=np.float64) self.estimator_errors_ = np.array([], dtype=np.float64)
return return
# probablement la list de h de t global que l'on a a la fin
self.estimators_ = [] self.estimators_ = []
# modification mu cumbo
# mettre deux dim sur n_estimators * n_views
self.estimator_weights_alpha_ = np.zeros((self.n_iterations_, n_views), dtype=np.float64) self.estimator_weights_alpha_ = np.zeros((self.n_iterations_, n_views), dtype=np.float64)
self.estimator_weights_beta_ = np.zeros((self.n_iterations_, n_views, self.n_classes_), dtype=np.float) self.estimator_weights_beta_ = np.zeros((self.n_iterations_, n_views, self.n_classes_), dtype=np.float)
self.estimator_errors_ = np.zeros((n_views, self.n_iterations_), dtype=np.float64) self.estimator_errors_ = np.zeros((n_views, self.n_iterations_), dtype=np.float64)
...@@ -473,7 +464,7 @@ class MuComboClassifier(BaseEnsemble, ClassifierMixin, UBoosting): ...@@ -473,7 +464,7 @@ class MuComboClassifier(BaseEnsemble, ClassifierMixin, UBoosting):
predicted_classes, score_function_dif, betas, n_yi) = self._init_var(n_views, y) predicted_classes, score_function_dif, betas, n_yi) = self._init_var(n_views, y)
self.n_yi_ = n_yi self.n_yi_ = n_yi
for current_iteration in range(self.n_iterations_): for current_iteration in range(self.n_iterations_):
# list de h pris a l'etape t # list of h at stage t
dist = self._compute_dist(cost, y) dist = self._compute_dist(cost, y)
# get h_t _i with edges delta # get h_t _i with edges delta
for ind_view in range(n_views): for ind_view in range(n_views):
...@@ -488,13 +479,11 @@ class MuComboClassifier(BaseEnsemble, ClassifierMixin, UBoosting): ...@@ -488,13 +479,11 @@ class MuComboClassifier(BaseEnsemble, ClassifierMixin, UBoosting):
# end of choose cost matrix # end of choose cost matrix
# TO DO estimator_errors_ estimate # TO DO estimator_errors_ estimate
########################################### ###########################################
#self.estimator_errors_[current_iteration] = to do
#############self.estimator_errors_[current_iteration] = to do
# update C_t de g # update C_t de g
edges = self._compute_edges(cost, predicted_classes, y) edges = self._compute_edges(cost, predicted_classes, y)
alphas = self._compute_alphas(edges) alphas = self._compute_alphas(edges)
# modif mu cumbo
self.estimator_weights_alpha_[current_iteration, :] = alphas self.estimator_weights_alpha_[current_iteration, :] = alphas
betas = self._compute_betas(alphas, y, score_function_dif, predicted_classes) betas = self._compute_betas(alphas, y, score_function_dif, predicted_classes)
...@@ -540,7 +529,6 @@ class MuComboClassifier(BaseEnsemble, ClassifierMixin, UBoosting): ...@@ -540,7 +529,6 @@ class MuComboClassifier(BaseEnsemble, ClassifierMixin, UBoosting):
dec_func = np.zeros((n_samples, n_classes)) dec_func = np.zeros((n_samples, n_classes))
# update muCombo # update muCombo
# for ind_estimator in range(n_estimators):
for ind_estimator in range(n_estimators): for ind_estimator in range(n_estimators):
ind_iteration = ind_estimator // self.n_views_ ind_iteration = ind_estimator // self.n_views_
current_vue = ind_estimator % self.n_views_ current_vue = ind_estimator % self.n_views_
......
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